A hierarchical attention network for stock prediction based on attentive multi-view news learning

Abstract

Stock prediction with news released on media platform is helpful for investors to make good investment decisions. Recent researches are generally based on single news view, e.g., headline or body, as a predictive indicator and thus information received is insufficient or incomplete which also lacks of study on market information, then bring low performances of models. In this research, we propose a hierarchical attention network based on attentive multi-view news learning (NMNL) to excavate more useful information from news and the stock market for stock prediction. The core of our approach is a news encoder and a market information encoder. In the news encoder, we learn multi-view news information representation from news headlines, bodies and sentiments by regarding them as three independent parts. We find that the combination of headline, body and sentiment outperforms conventional models on single news view. In the market information encoder, we employ the attention mechanism to capture pivotal news information and combine technical indicators to represent representative market information. In addition, a temporal auxiliary based on Bi-directional Long Short-Term Memory (Bi-LSTM) model is used to generate the contextual market information for stock prediction. Extensive experiments demonstrate the superiority of NMNL, which outperforms state-of-the-art stock prediction solutions.